- Introduction
- Data gathering & cleaning
- Descriptive statistics
- Linear Regressions (CPU&GPU)
- XGBoost
- Points of further consideration
Mai 20th 2021
The 2020 US-Presidential election led to highest voter turnout in history due to clash of socio-economic groups and ideologies:
Conservative vs. Liberal
Urban vs. Rural
Climate Protectionists vs. Climate Change Deniers
Young vs. old
But are those socio-economic gaps also visible when it comes to the American’s love for big cars?
Used Car dataset (Kaggle)
Total file size of ~9.3GB
Two data sets for the voting outcome on a Precinct level and on a State level [MIT Election Lab]
Total size for both files ~0.2GB
Problem : county level voting data vs. longitudonale/lattitudonale level car data
Solution: Package ‘jvamisc’ maps latitudinal & longitudinal car data to county
city fuel economy < 70 miles per gallon
highway fuel economy < 60 miles per gallon
Horsepower < 600
Price < 200’000 $
Mileage < 300’000 miles
rpm (revolutions per minute) < 2000
Savings Amount < 2500
year > 1900
\(\frac{democraticvotes}{democratic votes + republican votes}\)
Is new (if car is new or pre-owned)
Price
Fuel economy city (fuel consumption in the city)
Mileage
Horsepower
Length
Max seating
Body type
Brand name
State